During volume rendering, the interior volume is also visualized, i.e. not merely a surface of an inhomogeneous object, with the result that transparency effects and/or internal structures can be realistically reproduced. The three-dimensional object is here represented by way of volume data in three-dimensional resolution.
A known method for volume rendering is what is referred to as ray casting, in which the propagation of imaginary rays, referred to below as visual rays, is simulated, which emerge from the eye of an imaginary observer or an imaginary detector and pass through the object to be visualized. Illumination values for points within the object are ascertained along the visual rays. Finally, a visualized two-dimensional image is composed from the illumination values that were ascertained for the visual rays.
Realistic visualization requires that effects of the global illumination, for example environmental occlusion, shadows, translucency, what is known as color bleeding, surface shading, complex camera effects and/or illumination by way of any desired ambient light conditions, are taken into account as comprehensively as possible. Environmental occlusion is here frequently also referred to as ambient occlusion. Such illumination effects contribute, in particular in volume rendering, significantly to the depth and shape perception and thus to improved image understanding.
Known volume rendering systems frequently take into account local illumination effects, but are unable to realistically incorporate all global illumination effects.
The document “Exposure Render: An Interactive Photo-Realistic Volume Rendering Framework” by Thomas Kroes et al., PLoS ONE, volume 7, issue 7, dated July 2012, discloses a volume rendering method which uses a Monte Carlo simulation together with what is known as Woodcock tracking for tracing visual rays. However, to calculate realistic shading effects, in addition to a respective visual ray further rays need to be traced within the object volume, which requires a significant computational outlay. Moreover, what is known as importance sampling is necessary, which influences the statistical properties of the Monte Carlo method.